How Technology Contributes to Value Creation

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗣𝗿𝗼𝗳𝗶𝘁 𝗖𝗲𝗻𝘁𝗲𝗿: 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗮𝘁𝗵 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 Most board conversations about technology still frame it as a cost center. This legacy perspective is increasingly dangerous in a market where technology-driven revenue streams now represent the primary growth engine for market leaders. After leading digital value creation initiatives across multiple enterprises, I've observed a fundamental shift in how successful organizations measure technology's contribution to enterprise value. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻: 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗖𝗼𝘀𝘁 For decades, execs evaluated technology through the lens of: • Cost reduction (improve efficiency) • Risk mitigation (maintain stability) • Capital expense management (minimize spend) This framework produced predictable outcomes: technology budgets constrained to 2-5% of revenue, innovation limited to incremental improvements, and strategic discussions focused on cost containment rather than value creation. 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻: 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗩𝗮𝗹𝘂𝗲 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿 (business accelerator) Market-leading organizations now evaluate technology through a fundamentally different formula: 1. Revenue multiplication (over cost reduction) 2. Margin expansion (over operational efficiency) 3. Valuation multiple enhancement (over capital management) This framework produces dramatically different outcomes. When we implemented this model at one healthcare organization, technology investments shifted from 4% to 8% of revenue—while increasing EBITDA by 14%. 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆'𝘀 𝗣&𝗟 𝗜𝗺𝗽𝗮𝗰𝘁 The organizations achieving exponential returns apply three specific calculations: 1. Revenue per digital channel: One financial services firm discovered their digital-first customers generated 2.8x higher lifetime value than traditional channels. This insight transformed their technology roadmap from cost management to revenue acceleration. 2. Margin by technology enablement tier: A manufacturing company segmented product lines by technology enablement level, revealing a direct correlation between digital capabilities and margin expansion—from 12% to 38% across tiers. 3. Valuation premium from technical architecture: Companies with modular, API-first architectures command 2-3x higher valuation multiples than legacy competitors—a metric now explicitly tracked in board-level technology reporting. Organizations that measure technology as a profit center outperform those that measure it as a cost center by 340% over a five-year horizon. This is not mere thought leadership! I've implemented this framework across multiple organizations, transforming technology's position from cost burden to value driver. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘝𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

Is AI flattening organizations? Not because it removes managers. But because everyone becomes one. AI gives everyone their own workforce. ▪️ Before AI, organizations scaled by adding team members. ▪️ After AI, organizations scale by improving judgment. AI reveals that management was never about hierarchy. Management is about creating value through: ▪️ Prioritization ▪️ Delegation ▪️ Coaching ▪️ Quality control The implication is profound. ▪️ Human value shifts from: Doing → Directing ▪️ Or perhaps more accurately: Operating → Judging Human judgment is continually shifted as AI continuously pushes humans toward higher-order forms of judgment. ▪️ AI generates the options. ▪️ Humans decide which option creates value. Human potential is not replaced. Human judgment is continuously relocated. PS: Imagine the impact on value creation. ▪️ Before AI: 100 employees × 1 value opportunity explored/month = 100 ▪️ After AI: 100 employees × 10 value opportunities explored/month = 1,000

Every aspect of organizational value creation will be Humans + AI. Our focus needs to be on architecting how they are integrated across all layers, and building loops where humans, AI, and systems all continually improve their capabilities. The Individual Augmentation layer is already happening apace by giving GenAI tools to knowledge workers. What is often still missing are the skills and mindsets for effective use and leverage. Many are now recognizing the reality that Humans + AI teams are moving to the center of work, requiring redesigned workflows, new skills, effective governance and accountability structures, and structures for better decision-making. Workflows must be redesigned to bring to bear the most relevant capabilities across the organization as work becomes more fluid, requiring Humans + AI orchestration processes to dynamically address challenges and opportunities. Every aspect of the organization needs to evolve based on data, insight, and learning. Learning loops need to encompass humans, AI systems, and how each of the other layers are architecting in constantly evolving systems. And since value creation largely happens across organizational boundaries, not just within companies, Humans + AI processes need to enable shared value creation, based on interoperability, trust layers, and platform structures. This diagram is an evolution from a previous one I created, simplifying and consolidating based on extensive work with leaders in helping them apply Humans + AI principles to transform their organizations. Would love any thoughts and comments

Reflections from My MBA: Understanding My Role and the Impact of AI During my MBA, I encountered a powerful framework from Harvard Business Review by Mark Huselid, Richard Beatty, and Brian Becker. They proposed that organizations should focus not only on finding A players, but on identifying A positions — roles that are both strategically important and show high variability in performance. This framework classifies jobs into three types, each revealing how AI can transform work and value creation: 🔹 A Positions (Strategic) These roles have high strategic importance and high performance variation. Excellence here drives disproportionate value. AI acts as an amplifier, expanding foresight, enhancing decision intelligence, and accelerating innovation. Predictive analytics and large language models now allow leaders to anticipate risks, test scenarios, and act with greater clarity. 🔹 B Positions (Support) These are enabling roles that support strategy or minimize operational risk. AI serves as an optimizer, improving coordination, personalizing learning, and standardizing processes. In education, AI supports adaptive learning and feedback systems. In management, it helps monitor performance and streamline workflows. 🔹 C Positions (Surplus) These are operational or routine roles with low strategic impact and minimal variation in performance. AI functions as an automator, handling repetitive administrative tasks, streamlining documentation, and improving accuracy. By freeing people from low-value work, AI allows more time for creativity, empathy, and strategy. Reflecting on my current portfolio, I realize I span all three categories: As a Health Services Researcher and Clinical AI Advisor, I operate in an A position where AI magnifies the impact of data-driven decisions on population health and policy. As an educator and programme coordinator, I work in a B position where AI enhances learning and capability development. In administrative tasks, I rely on automation to reclaim time for reflection and innovation. This reflection taught me that the future of talent management lies in human–AI synergy. The key is not to resist automation, but to use it to amplify what matters most — insight, creativity, and purpose. As leaders, we should ask: Which parts of our work are truly strategic? How can AI help us perform with greater impact and clarity? Are we empowering others to use AI as a force multiplier for value creation? Call to Action: Reflect on your own role. Where do you stand in the A–B–C spectrum, and how is AI reshaping your ability to create impact? Share your perspective and inspire others to lead more intelligently in the age of AI. #Leadership #ArtificialIntelligence #TalentManagement #APlayers #APositions #MBA #FutureOfWork #PublicHealth #OrganizationalStrategy #DrAngYeeGary

𝗛𝗼𝘄 𝗧𝗕𝗠 𝗣𝗼𝘄𝗲𝗿𝘀 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 These days, every business is, at least partly, a technology business. Whether you’re a manufacturer, an energy company, or a telecom provider, technology is central to how you create value and stay competitive. But managing tech is tricky. It’s not just about keeping costs down—it’s about making sure every tech investment is pulling its weight for your business. Many companies are still looking at tech costs in isolation instead of tying them to business goals. That’s where a smarter management approach like TBM comes into play. It’s about taking a holistic view of your technology landscape to drive efficiency, boost profits, and fuel long-term growth. 𝗦𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗲 𝗪𝗵𝗼𝗹𝗲 𝗣𝗶𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗚𝗿𝗼𝘄𝘁𝗵 One of the biggest advantages of a well-structured tech management approach is visibility. Imagine if you could see every cost, every investment, and every technology’s impact on your business in one clear snapshot. Take the case of a manufacturing company. If you're looking at production costs, you need more than just the price of raw materials and labor. You want to understand the tech driving your operations: the software that handles scheduling, the sensors monitoring your machinery, and the systems analyzing your supply chain data. Seeing how all of this fits together helps you make smarter decisions, like investing in better automation tools that reduce downtime and drive efficiency. 𝗟𝗶𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 𝘁𝗼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗚𝗼𝗮𝗹𝘀 Technology investments only make sense if they support your broader business strategy—whether that's scaling operations, enhancing customer experience, or increasing profitability. Smart tech management is about finding value and then reinvesting those savings into future growth. You get clarity on what's draining your budget and what’s truly driving success. Those insights let you cut unnecessary costs and channel the savings into the areas that matter most—like innovation, product development, or new market opportunities. 𝗛𝗼𝘄 𝗧𝗕𝗠 𝗗𝗿𝗶𝘃𝗲𝘀 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗚𝗿𝗼𝘄𝘁𝗵 The mission of Technology Business Management (TBM) Council has always been to maximize the value of technology investments across every corner of a company. TBM’s real power lies in how it brings together frameworks like FinOps, ITAM, and a range of strategic practices, making them all work in sync to drive smarter, faster business decisions. It’s like pulling every department—finance, tech, and operations—onto the same page to make sure the full potential of each investment is realized. The right framework brings all the puzzle pieces together—cloud, software, hardware, on-prem infrastructure—and shows how they drive your business. By aligning your tech with your strategy, you can turn technology from a budget line item into a catalyst for growth. #TBM

95% of teens have smartphones, and half report being online "almost constantly" — a 24% increase in just a decade. The knee-jerk reaction? "Less screen time." But what if that's the wrong approach? Instead of "How do we reduce screen time?" perhaps we should be asking: "How do we transform screen time into something valuable?" At our tech schools across America, we've discovered that deliberate screen time can actually double learning speed. The data proves it: Our Brownsville school took kids from the 31st percentile to the 86th in just one year. The 5 Elements of Transformative Screen Time 1. Creation Over Consumption Our 3rd graders don't watch YouTube - they: • Produce news broadcasts • Build business plans with ChatGPT • Program self-driving cars and drones • Create school ambassador presentations 2. AI-Powered Personalization Every student gets a custom AI tutor that: • Adapts to their exact level • Adjusts material in real-time • Identifies knowledge gaps instantly • Tracks genuine mastery (not memorization) 3. Strategic Time Limits The secret is just 2 hours of focused tech learning daily. The rest is hands-on projects and real-world skills. This isn't theory—we've proven it across 10+ schools. 4. Building Status Through Contribution Research shows teens desperately need to feel competent and valuable. We transform passive scrolling into active creation, where students build real confidence through meaningful digital contributions. 5. Adult-Guided Innovation Parents and teachers don't just monitor—they collaborate: • Join coding projects • Review business plans • Guide content creation • Shape tech habits actively What have our results been? Students are more engaged, learning faster, and developing skills they'll actually use. The digital world isn't going away anytime soon. Traditional schools use tech to deliver the same old lectures. We use it to unleash potential. The challenge isn't screen time itself. It's teaching kids to use technology as a tool for growth instead of an escape from boredom. Because the next generation of entrepreneurs, creators, and innovators won't come from less screen time. They'll come from better screen time.

Two new pieces on AI strategy are telling a very consistent story today. Bain & Company’s “AI Enterprise: Code Red” argues that the real risk isn’t falling behind on models—it’s treating AI as a tech project instead of a rewiring of how the enterprise creates value and makes decisions. The Harvard Business Review/Boston Consulting Group (BCG) article, “Look for New Ways to Create Value When Deploying Gen AI,” shows that across 800 U.S. firms, productivity gains from gen AI are already being competed away in many sectors; margins aren’t moving because most companies are just doing the same work, slightly faster. Put together, the message is pretty blunt: ▪️ Efficiency is table stakes; it rarely delivers durable advantage. ▪️ Gen AI that doesn’t change the design of work, products, and business models will quietly commoditize you. ▪️ The real opportunity is using AI to unlock new value pools—new services, new ways to bundle and price, new experiences—not just cheaper versions of existing ones. If you’re leading an AI agenda right now, a useful gut check might be: 👉 How much of your AI roadmap is focused on cost and throughput vs. creating new value you couldn’t credibly offer two years ago? Linking both articles in the comments—worth the read if you’re still pitching AI as “productivity” instead of “strategic re-architecture.” #GenerativeAI #DigitalTransformation #ValueCreation #Leadership #BusinessStrategy Image by: Towfiqu Barbhuiya

In this playbook, you’ll find a series of insights from business leaders across sectors that reinforce the sustainability business case. The examples are grounded in operational decisions. A logistics facility in Mexico integrates a 619 kW solar system, generating ~892 MWh annually and reducing exposure to energy price volatility. The initial deployment leads to additional installations across the portfolio. A regenerative agriculture program across 100,000 acres targets ~150,000 metric tons of CO₂ reduction over five years, while improving soil productivity and long term supply security. A large commercial building reduces energy use by 20%, unlocking up to $1.5M in annual savings and more than $14M in incentives through energy storage, controls and heat recovery. AI driven HVAC optimization across retail locations delivers ~8 million kWh in savings in one year, with over $1M in cost reduction, without requiring major capital upgrades. Across these cases, sustainability decisions translate into: * Lower operating costs * Reduced exposure to volatility * Improved asset performance * New revenue opportunities * Stronger resilience across the value chain Nearly 90% of executives indicate a willingness to pay a premium for sites with reliable energy infrastructure. Energy security is now influencing location decisions more than labor costs. Infrastructure reliability, procurement models and energy performance are starting to influence demand, not just efficiency. AI and connected systems are being used to optimize energy demand in real time, extend asset life, and reduce maintenance costs, with measurable impact on both cost and performance. Value creation is tied to how sustainability is embedded into operations, procurement and asset management, and how outcomes are tracked in financial terms. The commercial layer remains less defined. How these capabilities translate into pipeline creation, deal velocity and customer preference is not yet consistently captured.

I hear technology called a “cost center” a lot in PE‑backed companies. If I’m honest, I get why. 🎢 At Flow Control Group, IT didn’t start where it needed to be either. We weren’t fully aligned to a value creation strategy on day one — and we’re still not “done.” 🏗️ But what’s changed is that we’re clear on where we want to go, and we have a plan to get there. 🗺️ That shift matters more than perfection. Over time, it’s become obvious that tech only creates value when it’s intentionally tied to how the business is trying to win. For us, that’s showing up in a few ways: ⬆️ Growth gets easier when platforms are standardized and acquisitions can integrate without custom work every single time. When tech scales cleanly, it stops slowing the business down. 📈 Margins improve when infrastructure is simplified, cloud spend is disciplined, and vendor sprawl gets addressed. It’s not flashy work — but it absolutely shows up in EBITDA. 🌟 And reliability? Still underrated. Fewer incidents, better security, more predictable operations. Less noise. Less risk. More focus on running the business. The biggest learning for me in a PE environment has been this: 🎓 You don’t need to have everything perfect — but you do need to know where you’re headed and build intentionally toward it. 💲 Technology becomes a value creator when it grows up alongside the business, aligned to the value creation plan, not chasing it after the fact. ❓❓ Curious how others are navigating this — especially those still in the middle of the journey, not at the finish line.

Case Study: We recently worked with a SaaS business at a critical inflection point —the executive team knew they needed to transform their business to stay ahead, but they were grappling with a complex technology and data environment making it difficult to optimize their finance function and gain visibility into fundamental performance KPIs. Here's how Celeborn Capital approached the challenge: 1️⃣ Uncovered Critical Insights: We dove into the company's financial and operational data to identify KPIs that were crucial for driving growth. By standardizing and prioritizing these KPIs, we created executive-level dashboards providing clear, actionable insights. 2️⃣ Aligned Leadership: It was essential to get everyone on the same page. We worked closely with leadership and teams across the business to align on the most impactful initiatives. This included developing a robust value creation target focused on improving revenue and expense profile, ensuring that everyone was speaking from the same set of facts and was clear on direction. 3️⃣ Optimized Revenue Operations Leveraging existing technology, we developed a detailed plan to enhance revenue operations. This included improving customer analytics to reduce churn and boost net dollar retention, driving profitability at both the customer and product level. 4️⃣ Implemented a Sustainable Process: Beyond the immediate fixes, we established a long-term process for reviewing insights from the dashboards and acting on them. This systematic approach allowed the company to continuously optimize performance and make informed decisions swiftly. The Result? The company not only enhanced its enterprise value but also gained a sustainable process for improving decision-making and response time. The transformation led to significant revenue enhancement and cost savings, positioning the company for long-term success. It's not just about having the right tools—it's about using them effectively to drive real, measurable results. This case study is a testament to the power of aligning strategy with execution, leveraging data-driven insights, and focusing relentlessly on value creation.